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Neural Networks in Software and Hardware Testing


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Past members:

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Funding

This work has been funded by NSF grants

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Neural Networks in Software Testing

A difficult step in the testing of software or hardware is the choice of test cases to probe the behavior of the system. Anneliese von Mayrhauser, Rick Mraz, and I show how a neural network trained to predict software faults can be inverted to predict additional test cases that are likely to produce additional faults:

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Neural Networks in Hardware Testing

With Tom Chen, we have also applied the same technique to generate test stimuli to VHDL models of hardware designs:

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Neural Networks in Software and Hardware Testing Research in CS at CSU, Charles W. Anderson / anderson@cs.colostate.edu

Copyright © 1998 Charles Anderson